Fast R implementation of Gini mean difference

You can avoid the for loops with outer, but that will use more
memory.

gmd <- 
   function(x, w) 0.5 * sqrt(pi) * sum(w * abs(outer(x, x, "-")))
/
       ((length(x)-1)*sum(w))


On Wednesday 23 April 2003 10:17 pm, Andrew C. Ward wrote:

I have written the following function to calculate the weighted

mean

difference for univariate data (see
http://www.xycoon.com/gini_mean_difference.htm for a related
formula). Unsurprisingly, the function is slow (compared to sd or

mad)

for long vectors. I wonder if there's a way to make the function
faster, short of creating an external C function. Thanks very

much

for your advice.


gmd <- function(x, w) { # x=data vector, w=weights vector
   n <- length(x)
   tmp <- 0
   for (i in 1:n) {
      for (j in 1:n) {
         tmp <- tmp + w[i]*abs(x[i]-x[j])
      }
   }
   retval <- 0.5*sqrt(pi)*tmp/((n-1)*sum(w))
}

gmd(rnorm(100))



Regards,

Andrew C. Ward

CAPE Centre
Department of Chemical Engineering
The University of Queensland
Brisbane Qld 4072 Australia
andreww at cheque.uq.edu.au